Advanced Parameter Estimation Tools for Building Mathematical Models of Chemical Processes

Engineers use mathematical models to describe the production of plastics and other chemicals. The models contain unknown parameters that are estimated from plant data. In the past year, the research team analyzed several criteria that modelers use to decide how complex or how simplified their models should be. They showed that one popular model-selection criterion, the corrected Akaike Information Criterion, tends to select very simple models, and that another, the adjusted coefficient of determination, tends to select models with many parameters. The team developed a new method for estimating parameters in differential-equation models that describe the time-varying behaviour of chemical production processes. A key benefit of the proposed method is that it provides information to modelers about whether the deficiencies in model predictions arise mainly from uncertainties in the measurements or from deficiencies in the model equations. The resulting information about model mismatch will be useful to engineers who use models for monitoring and control of chemical production facilities.